Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, California, United States.
Signal and Image Processing Institute, University of Southern California, California, United States.
NMR Biomed. 2020 Dec;33(12):e4244. doi: 10.1002/nbm.4244. Epub 2020 Jan 7.
Multiexponential modeling of relaxation or diffusion MR signal decays is a popular approach for estimating and spatially mapping different microstructural tissue compartments. While this approach can be quite powerful, it is also limited by the fact that one-dimensional multiexponential modeling is an ill-posed inverse problem with substantial ambiguities. In this article, we present an overview of a recent multidimensional correlation spectroscopic imaging approach to this problem. This approach helps to alleviate ill-posedness by making advantageous use of multidimensional contrast encoding (e.g., 2D diffusion-relaxation encoding or 2D relaxation-relaxation encoding) combined with a regularized spatial-spectral estimation procedure. Theoretical calculations, simulations, and experimental results are used to illustrate the benefits of this approach relative to classical methods. In addition, we demonstrate an initial proof-of-principle application of this kind of approach to in vivo human MRI experiments.
多指数模型可以对弛豫或扩散磁共振信号的衰减进行建模,这是一种用于估计和空间绘制不同微观结构组织隔室的常用方法。尽管这种方法非常强大,但它也受到限制,因为一维多指数模型是一个具有很大歧义的不适定反问题。在本文中,我们介绍了一种最近的多维相关光谱成像方法来解决这个问题。这种方法通过有利地利用多维对比编码(例如,二维扩散-弛豫编码或二维弛豫-弛豫编码)结合正则化的空间-谱估计过程,有助于缓解不适定性。理论计算、模拟和实验结果用于说明与经典方法相比,该方法的优势。此外,我们还展示了这种方法在体内人体 MRI 实验中的初步原理验证应用。